we give some notations here. \(\lbrace (I_s^{(i)} \in \mathbb {R}^{3 \times H \times W},I_t^{(i)}\in \mathbb {R}^{3 \times H \times W})\rbrace _{i=1,...,N}\)  denotes pairs of source-target images of same person in different poses. \(H\)  is the height of the image, \(W\)  is the width of the image, \(N\)  is the number of person images. For each pair \((I_s,I_t)\) , a keypoint-based source and a keypoint-based target pose \(P_s\in \mathbb {R}^{18 \times H \times W}\)  and \(P_t\in \mathbb {R}^{18 \times H \times W}\)  is extracted from the correspondence image, which
encoder pose as 18-channel heatmap representing 18 joints of a human body. We adopt the same Pose Estimator [1]} used in [2]}. What's more, we extracte semantic map pairs \((S_s,S_t)\)  of image pairs \((I_s,I_t)\)  with an state-of-the-art Human parser [3]} and converts semantic map \(S_s\)  into \(K\) -channel heatmap \(M_s \in \mathbb {R}^{K \times H \times W}\)  as [4]}, each channel is a mask map, corresponding to an attribute of the human body.
